# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras SavedModel deserialization."""

import re
import types
import warnings

import tensorflow.compat.v1.logging as logging
import tensorflow.compat.v2 as tf
from google.protobuf import message

from keras import backend
from keras import regularizers
from keras.engine import input_spec
from keras.optimizers.optimizer_v2 import optimizer_v2
from keras.protobuf import saved_metadata_pb2
from keras.protobuf import versions_pb2
from keras.saving import saving_utils
from keras.saving.saved_model import constants
from keras.saving.saved_model import json_utils
from keras.saving.saved_model import utils
from keras.saving.saved_model.serialized_attributes import CommonEndpoints
from keras.utils import generic_utils
from keras.utils import layer_utils
from keras.utils import metrics_utils
from keras.utils import tf_inspect
from keras.utils.generic_utils import LazyLoader

# To avoid circular dependencies between keras/engine and keras/saving,
# code in keras/saving must delay imports.

# TODO(b/134426265): Switch back to single-quotes to match the rest of the file
# once the issue with copybara is fixed.

models_lib = LazyLoader("models_lib", globals(), "keras.models")
base_layer = LazyLoader("base_layer", globals(), "keras.engine.base_layer")
layers_module = LazyLoader("layers_module", globals(), "keras.layers")
input_layer = LazyLoader("input_layer", globals(), "keras.engine.input_layer")
functional_lib = LazyLoader(
    "functional_lib", globals(), "keras.engine.functional"
)
training_lib = LazyLoader("training_lib", globals(), "keras.engine.training")
training_lib_v1 = LazyLoader(
    "training_lib_v1", globals(), "keras.engine.training_v1"
)
metrics = LazyLoader("metrics", globals(), "keras.metrics")
base_rnn = LazyLoader("base_rnn", globals(), "keras.layers.rnn.base_rnn")


PUBLIC_ATTRIBUTES = CommonEndpoints.all_functions.union(
    CommonEndpoints.all_checkpointable_objects
)
PUBLIC_ATTRIBUTES.add(constants.KERAS_ATTR)


def load(path, compile=True, options=None):
    """Loads Keras objects from a SavedModel.

    Any Keras layer or model saved to the SavedModel will be loaded back
    as Keras objects. Other objects are loaded as regular trackable objects
    (same as `tf.saved_model.load`).

    Currently, Keras saving/loading only retains the Keras object's weights,
    losses, and call function.

    The loaded model can be re-compiled, but the original optimizer, compiled
    loss functions, and metrics are not retained. This is temporary, and
    `model.save` will soon be able to serialize compiled models.

    Args:
      path: Path to SavedModel.
      compile: If true, compile the model after loading it.
      options: Optional `tf.saved_model.LoadOptions` object that specifies
        options for loading from SavedModel.

    Returns:
      Object loaded from SavedModel.
    """
    # TODO(kathywu): Add saving/loading of optimizer, compiled losses and
    # metrics.
    # TODO(kathywu): Add code to load from objects that contain all endpoints

    # Look for metadata file or parse the SavedModel
    metadata = saved_metadata_pb2.SavedMetadata()
    meta_graph_def = tf.__internal__.saved_model.parse_saved_model(
        path
    ).meta_graphs[0]
    object_graph_def = meta_graph_def.object_graph_def
    path_to_metadata_pb = tf.io.gfile.join(path, constants.SAVED_METADATA_PATH)
    if tf.compat.v1.gfile.Exists(path_to_metadata_pb):
        try:
            with tf.io.gfile.GFile(path_to_metadata_pb, "rb") as f:
                file_content = f.read()
            metadata.ParseFromString(file_content)
        except message.DecodeError as e:
            raise IOError(
                f"Cannot parse keras metadata at path {path_to_metadata_pb}: "
                f"Received error: {e}"
            )
    else:
        logging.warning(
            "SavedModel saved prior to TF 2.5 detected when loading "
            "Keras model. Please ensure that you are saving the model "
            "with model.save() or tf.keras.models.save_model(), *NOT* "
            "tf.saved_model.save(). To confirm, there should be a file "
            'named "keras_metadata.pb" in the SavedModel directory.'
        )
        _read_legacy_metadata(object_graph_def, metadata, path)

    if not metadata.nodes:
        # When there are no Keras objects, return the results from the core
        # loader
        return tf.saved_model.load(path, options=options)

    metadata = _update_to_current_version(metadata)
    # Recreate layers and metrics using the info stored in the metadata.
    keras_loader = KerasObjectLoader(metadata, object_graph_def)
    keras_loader.load_layers(compile=compile)

    # Generate a dictionary of all loaded nodes.
    nodes_to_load = {"root": None}
    for node_id, loaded_node in keras_loader.loaded_nodes.items():
        nodes_to_load[keras_loader.get_path(node_id)] = loaded_node
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", message="Trying to load ShardedVariables"
        )
        loaded = tf.__internal__.saved_model.load_partial(
            path, nodes_to_load, options=options
        )

    # Finalize the loaded layers and remove the extra tracked dependencies.
    keras_loader.finalize_objects()
    keras_loader.del_tracking()

    model = loaded["root"]

    if isinstance(model, training_lib.Model) and compile:
        # TODO(kathywu): Use compiled objects from SavedModel, instead of
        # creating new objects from the training config.
        training_config = model._serialized_attributes["metadata"].get(
            "training_config", None
        )
        if training_config is not None:
            model.compile(
                **saving_utils.compile_args_from_training_config(
                    training_config
                ),
                from_serialized=True,
            )
            saving_utils.try_build_compiled_arguments(model)
            if isinstance(model.optimizer, optimizer_v2.OptimizerV2):
                if model.optimizer.get_slot_names():
                    logging.warning(
                        "Your optimizer uses slots. "
                        "Slots cannot be restored from saved_model, "
                        "as a result, your model is starting with  "
                        "a new initialized optimizer."
                    )
        else:
            logging.warning(
                "No training configuration found in save file, so the "
                "model was *not* compiled. Compile it manually."
            )

    # Force variables and resources to initialize.
    if not tf.executing_eagerly():
        sess = backend.get_session()  # Variables are initialized by this call.
        sess.run(
            tf.compat.v1.get_collection(
                tf.compat.v1.GraphKeys.TABLE_INITIALIZERS
            )
        )

    return model


def _update_to_current_version(metadata):
    """Applies version updates to the metadata proto for backwards compat."""
    for node in metadata.nodes:
        if node.version.producer == 1 and node.identifier in [
            constants.MODEL_IDENTIFIER,
            constants.SEQUENTIAL_IDENTIFIER,
            constants.NETWORK_IDENTIFIER,
        ]:
            node_metadata = json_utils.decode(node.metadata)
            save_spec = node_metadata.get("save_spec")

            if save_spec is not None:
                node_metadata["full_save_spec"] = ([save_spec], {})
                node.metadata = json_utils.Encoder().encode(node_metadata)
    return metadata


def _read_legacy_metadata(object_graph_def, metadata, path):
    """Builds a KerasMetadata proto from the SavedModel ObjectGraphDef."""
    # Older SavedModels store the metadata directly in the proto instead of the
    # separate pb file.
    node_paths = _generate_object_paths(object_graph_def)
    for node_id, proto in enumerate(object_graph_def.nodes):
        if (
            proto.WhichOneof("kind") == "user_object"
            and proto.user_object.identifier
            in constants.KERAS_OBJECT_IDENTIFIERS
        ):
            if not proto.user_object.metadata:
                raise ValueError(
                    "Unable to create a Keras model from SavedModel at "
                    f"{path}. This SavedModel was exported with "
                    "`tf.saved_model.save`, and lacks the Keras metadata file. "
                    "Please save your Keras model by calling `model.save` "
                    "or `tf.keras.models.save_model`. Note that "
                    "you can still load this SavedModel with "
                    "`tf.saved_model.load`."
                )
            metadata.nodes.add(
                node_id=node_id,
                node_path=node_paths[node_id],
                version=versions_pb2.VersionDef(
                    producer=1, min_consumer=1, bad_consumers=[]
                ),
                identifier=proto.user_object.identifier,
                metadata=proto.user_object.metadata,
            )


def _generate_object_paths(object_graph_def):
    """Traverses through an ObjectGraphDef and builds a map of all node
    paths."""
    paths = {0: "root"}
    nodes_to_visit = [0]

    while nodes_to_visit:
        current_node = nodes_to_visit.pop()
        current_path = paths[current_node]
        for reference in object_graph_def.nodes[current_node].children:
            if reference.node_id in paths:
                continue
            paths[reference.node_id] = f"{current_path}.{reference.local_name}"
            nodes_to_visit.append(reference.node_id)

    return paths


def _is_graph_network(layer):
    """Determines whether the layer is a graph network."""

    if isinstance(layer, RevivedNetwork):
        return False
    elif isinstance(layer, functional_lib.Functional):
        return layer._is_graph_network or isinstance(
            layer, models_lib.Sequential
        )
    return False


class KerasObjectLoader:
    """Loader that recreates Keras objects (e.g.

    layers, models).

    Layers and models are revived from either the config or SavedModel following
    these rules:
    1. If object is a graph network (i.e. Sequential or Functional) then it will
       be initialized using the structure from the config only after the
       children layers have been created. Graph networks must be initialized
       with inputs and outputs, so all child layers must be created beforehand.
    2. If object's config exists and the class can be found, then revive from
       config.
    3. Object may have already been created if its parent was revived from
       config. In this case, do nothing.
    4. If nothing of the above applies, compose the various artifacts from the
       SavedModel to create a subclassed layer or model. At this time, custom
       metrics are not supported.

    """

    def __init__(self, metadata, object_graph_def):
        self._metadata = {x.node_id: x for x in metadata.nodes}
        self._proto = object_graph_def

        self._node_paths = {
            node_data.node_id: node_data.node_path
            for node_data in metadata.nodes
        }
        self.loaded_nodes = {}  # Maps node path -> loaded node

        # Store all node ids that have already been traversed when tracking
        # nodes that were recreated from the config.
        self._traversed_nodes_from_config = set()

        # Maps model id -> (blank model obj, list of child layer or their node
        # ids) This tracks all layers in functional and sequential models. These
        # models are only reconstructed after all of their child layers have
        # been created.
        self.model_layer_dependencies = {}
        self._models_to_reconstruct = []

    def del_tracking(self):
        """Removes tracked references that are only used when loading the
        model."""
        # Now that the node object has been fully loaded, and the checkpoint has
        # been restored, the object no longer needs to track objects added from
        # SerializedAttributes. (Note that saving a training checkpoint still
        # functions correctly, because layers and variables are tracked
        # separately by the Layer object.)
        # TODO(kathywu): Instead of outright deleting these nodes (which would
        # make restoring from a different checkpoint tricky), mark them as extra
        # dependencies that are OK to overwrite.
        for node in self.loaded_nodes.values():
            node = node[0]
            if not isinstance(node, base_layer.Layer):
                # Loaded nodes can contain other trackable objects created when
                # loading layers from the config, such as variables.
                continue
            for name in PUBLIC_ATTRIBUTES:
                node._delete_tracking(name)

            if isinstance(node, functional_lib.Functional):
                # Delete the temporary layer dependencies, which were used to
                # restore the checkpointed values. When the model is live, the
                # user can delete or add layers to the model at any time, so
                # these layer dependencies may be obsolete.
                dependencies = list(node._self_unconditional_dependency_names)
                for name in dependencies:
                    if (
                        re.match(r"^layer(_with_weights)?-[\d+]", name)
                        is not None
                    ):
                        node._delete_tracking(name)

    def _add_children_recreated_from_config(self, obj, proto, node_id):
        """Recursively records objects recreated from config."""

        if node_id in self._traversed_nodes_from_config:
            return

        parent_path = self._node_paths[node_id]
        self._traversed_nodes_from_config.add(node_id)
        obj._maybe_initialize_trackable()
        if isinstance(obj, base_layer.Layer) and not obj.built:
            metadata = json_utils.decode(self._metadata[node_id].metadata)
            self._try_build_layer(
                obj, node_id, metadata.get("build_input_shape")
            )

        # Create list of all possible children
        children = []
        # Look for direct children
        for reference in proto.children:
            obj_child = obj._lookup_dependency(reference.local_name)
            children.append(
                (obj_child, reference.node_id, reference.local_name)
            )

        # Add metrics that may have been added to the layer._metrics list.
        # This is stored in the SavedModel as layer.keras_api.layer_metrics in
        # SavedModels created after Tf 2.2.
        metric_list_node_id = self._search_for_child_node(
            node_id, [constants.KERAS_ATTR, "layer_metrics"]
        )
        if metric_list_node_id is not None and hasattr(obj, "_metrics"):
            obj_metrics = {m.name: m for m in obj._metrics}
            for reference in self._proto.nodes[metric_list_node_id].children:
                metric = obj_metrics.get(reference.local_name)
                if metric is not None:
                    metric_path = "{}.layer_metrics.{}".format(
                        constants.KERAS_ATTR, reference.local_name
                    )
                    children.append((metric, reference.node_id, metric_path))

        for obj_child, child_id, child_name in children:
            child_proto = self._proto.nodes[child_id]

            if not isinstance(obj_child, tf.__internal__.tracking.Trackable):
                continue
            if (
                child_proto.user_object.identifier
                in tf.__internal__.saved_model.load.registered_identifiers()
            ):
                setter = tf.__internal__.saved_model.load.get_setter(
                    child_proto.user_object
                )
            elif (
                obj_child._object_identifier
                in constants.KERAS_OBJECT_IDENTIFIERS
            ):
                setter = _revive_setter
            else:
                setter = setattr

            if child_id in self.loaded_nodes:
                if self.loaded_nodes[child_id][0] is not obj_child:
                    # This means that the same trackable object is referenced by
                    # two different objects that were recreated from the config.
                    logging.warning(
                        "Looks like there is an object (perhaps variable or "
                        "layer) that is shared between different "
                        "layers/models. This may cause issues when restoring "
                        "the variable values. Object: {}".format(obj_child)
                    )
                continue

            # Overwrite variable names with the ones saved in the SavedModel.
            if (
                child_proto.WhichOneof("kind") == "variable"
                and child_proto.variable.name
            ):
                obj_child._handle_name = child_proto.variable.name + ":0"

            if isinstance(
                obj_child, tf.__internal__.tracking.TrackableDataStructure
            ):
                setter = lambda *args: None

            child_path = f"{parent_path}.{child_name}"
            self._node_paths[child_id] = child_path
            self._add_children_recreated_from_config(
                obj_child, child_proto, child_id
            )
            self.loaded_nodes[child_id] = obj_child, setter

    def load_layers(self, compile=True):
        """Load all layer nodes from the metadata."""
        # Load metrics after models and layers, since it's likely that models
        # and layers will create the metric when initialized (this avoids
        # wasting time by creating objects multiple times).
        metric_list = []
        for node_metadata in self._metadata.values():
            if node_metadata.identifier == constants.METRIC_IDENTIFIER:
                metric_list.append(node_metadata)
                continue

            self.loaded_nodes[node_metadata.node_id] = self._load_layer(
                node_metadata.node_id,
                node_metadata.identifier,
                node_metadata.metadata,
            )

        for node_metadata in metric_list:
            try:
                self.loaded_nodes[node_metadata.node_id] = self._load_layer(
                    node_metadata.node_id,
                    node_metadata.identifier,
                    node_metadata.metadata,
                )
            except ValueError as e:
                # Metrics are only needed when the model is compiled later. We
                # ignore errors when trying to load custom metrics when
                # `compile=False` until custom metrics are serialized properly
                # (b/135550038).
                if compile:
                    raise e
                logging.warning(
                    "Unable to restore custom metric. Please ensure that "
                    "the layer implements `get_config` and `from_config` "
                    "when saving. In addition, please use the "
                    "`custom_objects` arg when calling `load_model()`."
                )

    def _load_layer(self, node_id, identifier, metadata):
        """Load a single layer from a SavedUserObject proto."""
        metadata = json_utils.decode(metadata)

        # If node was already created
        if node_id in self.loaded_nodes:
            node, setter = self.loaded_nodes[node_id]

            # Revive setter requires the object to have a
            # `_serialized_attributes` property. Add it here.
            _maybe_add_serialized_attributes(node, metadata)

            config = metadata.get("config")
            if _is_graph_network(node) and generic_utils.validate_config(
                config
            ):
                child_nodes = self._get_child_layer_node_ids(node_id)
                self.model_layer_dependencies[node_id] = (node, child_nodes)
                if not child_nodes:
                    self._models_to_reconstruct.append(node_id)
            return node, setter

        # Detect whether this object can be revived from the config. If not,
        # then revive from the SavedModel instead.
        obj, setter = self._revive_from_config(identifier, metadata, node_id)
        if obj is None:
            obj, setter = revive_custom_object(identifier, metadata)

        # Add an attribute that stores the extra functions/objects saved in the
        # SavedModel. Most of these functions/objects are ignored, but some are
        # used later in the loading process (e.g. the list of regularization
        # losses, or the training config of compiled models).
        _maybe_add_serialized_attributes(obj, metadata)
        return obj, setter

    def _revive_from_config(self, identifier, metadata, node_id):
        """Revives a layer/model from config, or returns None."""
        if identifier == constants.METRIC_IDENTIFIER:
            obj = self._revive_metric_from_config(metadata)
        else:
            obj = self._revive_graph_network(
                identifier, metadata, node_id
            ) or self._revive_layer_or_model_from_config(metadata, node_id)

        if obj is None:
            return None, None

        setter = self._config_node_setter(_revive_setter)
        self._add_children_recreated_from_config(
            obj, self._proto.nodes[node_id], node_id
        )
        return obj, setter

    def _revive_graph_network(self, identifier, metadata, node_id):
        """Revives a graph network from config."""
        # Determine whether the metadata contains information for reviving a
        # functional or Sequential model.
        config = metadata.get("config")
        if not generic_utils.validate_config(config):
            return None

        class_name = tf.compat.as_str(metadata["class_name"])
        if generic_utils.get_registered_object(class_name) is not None:
            return None
        model_is_functional_or_sequential = (
            metadata.get("is_graph_network", False)
            or class_name == "Sequential"
            or class_name == "Functional"
        )
        if not model_is_functional_or_sequential:
            return None

        # Revive functional and sequential models as blank model objects for now
        # ( must be initialized to enable setattr tracking and attribute
        # caching).  Reconstruction of the network is deferred until all of the
        # model's layers have been revived.
        if class_name == "Sequential":
            model = models_lib.Sequential(name=config["name"])
        # The model is a custom Sequential model.
        elif identifier == constants.SEQUENTIAL_IDENTIFIER:
            # Uses the custom class name, since the config does not have one.
            model = models_lib.Sequential(name=class_name)
        else:
            model = models_lib.Functional(
                inputs=[], outputs=[], name=config["name"]
            )

        # Record this model and its layers. This will later be used to
        # reconstruct the model.
        layers = self._get_child_layer_node_ids(node_id)
        self.model_layer_dependencies[node_id] = (model, layers)
        if not layers:
            self._models_to_reconstruct.append(node_id)
        return model

    def _revive_layer_or_model_from_config(self, metadata, node_id):
        """Revives a layer/custom model from config; returns None if
        infeasible."""
        # Check that the following requirements are met for reviving from
        # config:
        #    1. Object can be deserialized from config.
        #    2. If the object needs to be built, then the build input shape can
        #       be found.
        class_name = metadata.get("class_name")
        config = metadata.get("config")
        shared_object_id = metadata.get("shared_object_id")
        must_restore_from_config = metadata.get("must_restore_from_config")
        if not generic_utils.validate_config(config):
            return None

        try:
            obj = layers_module.deserialize(
                generic_utils.serialize_keras_class_and_config(
                    class_name, config, shared_object_id=shared_object_id
                )
            )
        except (TypeError, KeyError) as e:
            # A name conflict has occurred. The `class_name` is in the Keras
            # native framework; however, the value in the framework is different
            # from the user's class definition which confuses the
            # KerasObjectLoader.
            builtin_layer = layers_module.get_builtin_layer(class_name)
            if builtin_layer:
                raise RuntimeError(
                    f"Unable to restore object of class '{class_name}' likely "
                    "due to name conflict with built-in Keras class "
                    f"'{builtin_layer}'. To override the built-in Keras "
                    "definition of the object, decorate your class with "
                    "`@keras.utils.register_keras_serializable` and include "
                    "that file in your program, or pass your class in a "
                    "`keras.utils.CustomObjectScope` that wraps this load call."
                ) from e
            else:
                raise
        except ValueError as e:
            if must_restore_from_config:
                raise e
            else:
                return None

        # Use the dtype, name, and trainable status. Often times these are not
        # specified in custom configs, so retrieve their values from the
        # metadata.

        obj._name = metadata["name"]
        if metadata.get("trainable") is not None:
            obj.trainable = metadata["trainable"]
        if metadata.get("dtype") is not None:
            obj._set_dtype_policy(metadata["dtype"])
        if metadata.get("stateful") is not None:
            obj.stateful = metadata["stateful"]
        if metadata.get("autocast") is not None:
            obj._autocast = metadata["autocast"]
        # Restore model save spec for subclassed models. (layers do not store a
        # SaveSpec)
        if isinstance(obj, training_lib.Model):
            full_save_spec = metadata.get("full_save_spec")
            if full_save_spec is not None:
                args_spec, kwargs_spec = full_save_spec
                inputs_spec = args_spec.pop(0)
                obj._set_save_spec(inputs_spec, args_spec, kwargs_spec)

        build_input_shape = metadata.get("build_input_shape")
        built = self._try_build_layer(obj, node_id, build_input_shape)

        if not built:
            # If the layer cannot be built, revive a custom layer instead.
            return None
        return obj

    def _revive_metric_from_config(self, metadata):
        """Revives a metric object using the config saved in the metadata."""
        class_name = tf.compat.as_str(metadata["class_name"])
        config = metadata.get("config")

        if not generic_utils.validate_config(config):
            return None

        try:
            obj = metrics.deserialize(
                generic_utils.serialize_keras_class_and_config(
                    class_name, config
                )
            )
        except ValueError:
            return None

        build_input_shape = metadata.get("build_input_shape")
        if build_input_shape is not None and hasattr(obj, "_build"):
            obj._build(build_input_shape)

        return obj

    def _try_build_layer(self, obj, node_id, build_input_shape):
        """Attempts to build the layer."""
        if obj.built or hasattr(obj.build, "_is_default"):
            obj.built = True
            return True

        if build_input_shape is None:
            build_input_shape = self._infer_inputs(
                node_id, convert_to_shapes=True
            )

        if build_input_shape is not None:
            obj.build(build_input_shape)
            base_layer.Layer.build(obj, build_input_shape)
            return True

        return False

    def get_path(self, node_id):
        return self._node_paths[node_id]

    def finalize_objects(self):
        """Finish setting up Keras objects.

        This function is executed after all objects and functions have been
        created.  Call functions and losses are attached to each layer, and once
        all layers have been fully set up, graph networks are initialized.

        Subclassed models that are revived from the SavedModel are treated like
        layers, and have their call/loss functions attached here.
        """
        # Finish setting up layers and subclassed models. This step attaches
        # call functions and losses to each object, and sets model
        # inputs/outputs.
        layers_revived_from_config = []
        layers_revived_from_saved_model = []
        for node_id, (node, _) in self.loaded_nodes.items():
            if (
                not isinstance(node, base_layer.Layer)
                # Don't finalize models until all layers have finished loading.
                or node_id in self.model_layer_dependencies
            ):
                continue

            self._unblock_model_reconstruction(node_id, node)

            if isinstance(node, input_layer.InputLayer):
                continue
            elif isinstance(node, metrics.Metric):
                continue

            if isinstance(node, (RevivedLayer, RevivedInputLayer)):
                layers_revived_from_saved_model.append(node)
            else:
                layers_revived_from_config.append(node)

        _finalize_saved_model_layers(layers_revived_from_saved_model)
        _finalize_config_layers(layers_revived_from_config)

        # Initialize graph networks, now that layer dependencies have been
        # resolved.
        self._reconstruct_all_models()

    def _unblock_model_reconstruction(self, layer_id, layer):
        """Removes layer from blocking model reconstruction."""
        for model_id, v in self.model_layer_dependencies.items():
            _, layers = v
            if layer_id not in layers:
                continue
            layers[layers.index(layer_id)] = layer
            if all(isinstance(x, base_layer.Layer) for x in layers):
                self._models_to_reconstruct.append(model_id)

    def _reconstruct_all_models(self):
        """Reconstructs the network structure of all models."""
        all_initialized_models = set()
        while self._models_to_reconstruct:
            model_id = self._models_to_reconstruct.pop(0)
            all_initialized_models.add(model_id)
            model, layers = self.model_layer_dependencies[model_id]
            self._reconstruct_model(model_id, model, layers)
            _finalize_config_layers([model])

        if all_initialized_models != set(self.model_layer_dependencies.keys()):
            # This should not happen.
            uninitialized_model_ids = (
                set(self.model_layer_dependencies.keys())
                - all_initialized_models
            )
            uninitialized_model_names = [
                self.model_layer_dependencies[model_id][0].name
                for model_id in uninitialized_model_ids
            ]
            raise ValueError(
                "Error loading model(s) in the SavedModel format. "
                "The following model(s) could not be initialized: "
                f"{uninitialized_model_names}"
            )

    def _reconstruct_model(self, model_id, model, layers):
        """Reconstructs the network structure."""
        config = json_utils.decode(self._metadata[model_id].metadata)["config"]

        # Set up model inputs
        if model.inputs:
            # Inputs may already be created if the model is instantiated in
            # another object's __init__.
            pass
        elif isinstance(model, models_lib.Sequential):
            if not layers or not isinstance(layers[0], input_layer.InputLayer):
                if config["layers"][0]["class_name"] == "InputLayer":
                    layers.insert(
                        0,
                        input_layer.InputLayer.from_config(
                            config["layers"][0]["config"]
                        ),
                    )
                elif "batch_input_shape" in config["layers"][0]["config"]:
                    batch_input_shape = config["layers"][0]["config"][
                        "batch_input_shape"
                    ]
                    layers.insert(
                        0,
                        input_layer.InputLayer(
                            input_shape=batch_input_shape[1:],
                            batch_size=batch_input_shape[0],
                            dtype=layers[0].dtype,
                            name=layers[0].name + "_input",
                        ),
                    )
            model.__init__(layers, name=config["name"])
            if not model.inputs:
                first_layer = self._get_child_layer_node_ids(model_id)[0]
                input_specs = self._infer_inputs(first_layer)
                input_shapes = self._infer_inputs(
                    first_layer, convert_to_shapes=True
                )
                model._set_inputs(input_specs)
                if not model.built and not isinstance(input_specs, dict):
                    model.build(input_shapes)
        else:  # Reconstruct functional model
            (
                inputs,
                outputs,
                created_layers,
            ) = functional_lib.reconstruct_from_config(
                config, created_layers={layer.name: layer for layer in layers}
            )
            model.__init__(inputs, outputs, name=config["name"])
            functional_lib.connect_ancillary_layers(model, created_layers)

        # Set model dtype.
        _set_network_attributes_from_metadata(model)

        # Unblock models that are dependent on this model.
        self._unblock_model_reconstruction(model_id, model)

    def _get_child_layer_node_ids(self, node_id):
        """Returns the node ids of each layer in a Sequential/Functional
        model."""
        # Sequential and Functional track layers with names following the format
        # "layer-N". Use this to generate the list of layers.
        num_layers = 0
        child_layers = {}
        pattern = re.compile("layer-(\\d+)")

        for child in self._proto.nodes[node_id].children:
            m = pattern.match(child.local_name)
            if m is None:
                continue
            layer_n = int(m.group(1))
            num_layers = max(layer_n + 1, num_layers)
            child_layers[layer_n] = child.node_id

        ordered = []
        for n in range(num_layers):
            child = child_layers.get(n)
            if child is None:
                break
            ordered.append(child)
        return ordered

    def _search_for_child_node(self, parent_id, path_to_child):
        """Returns node id of child node.

        A helper method for traversing the object graph proto.

        As an example, say that the object graph proto in the SavedModel
        contains an object with the following child and grandchild attributes:

        `parent.child_a.child_b`

        This method can be used to retrieve the node id of `child_b` using the
        parent's node id by calling:

        `_search_for_child_node(parent_id, ['child_a', 'child_b'])`.

        Args:
          parent_id: node id of parent node
          path_to_child: list of children names.

        Returns:
          node_id of child, or None if child isn't found.
        """
        if not path_to_child:
            return parent_id

        for child in self._proto.nodes[parent_id].children:
            if child.local_name == path_to_child[0]:
                return self._search_for_child_node(
                    child.node_id, path_to_child[1:]
                )
        return None

    def _infer_inputs(self, layer_node_id, convert_to_shapes=False):
        """Infers input shape of layer from SavedModel functions."""
        call_fn_id = self._search_for_child_node(
            layer_node_id, ["call_and_return_all_conditional_losses"]
        )
        if call_fn_id is None:
            return None

        concrete_functions = self._proto.nodes[
            call_fn_id
        ].function.concrete_functions
        if not concrete_functions:
            return None
        call_fn_name = concrete_functions[0]
        call_fn_proto = self._proto.concrete_functions[call_fn_name]
        structured_input_signature = tf.__internal__.saved_model.decode_proto(
            call_fn_proto.canonicalized_input_signature
        )
        inputs = structured_input_signature[0][0]
        if convert_to_shapes:
            return tf.nest.map_structure(lambda spec: spec.shape, inputs)
        else:
            return inputs

    def _config_node_setter(self, setter):
        """Creates edges for nodes that are recreated from config."""

        def setattr_wrapper(obj, name, value):
            # Avoid overwriting attributes of objects recreated from the config.
            if obj._lookup_dependency(name) is None:
                setter(obj, name, value)

        return setattr_wrapper


def _finalize_saved_model_layers(layers):
    """Runs the final steps of loading Keras Layers from SavedModel."""

    # 1. Set up call functions for all layers initialized from the SavedModel (
    # and not the config)
    for layer in layers:
        layer.built = True
        layer_call = getattr(
            _get_keras_attr(layer), "call_and_return_conditional_losses", None
        )
        if layer_call and layer_call.concrete_functions:
            call_spec = layer_utils.CallFunctionSpec(
                tf_inspect.getfullargspec(layer_call)
            )
            layer.call = utils.use_wrapped_call(
                layer, layer_call, call_spec, return_method=True
            )
            expects_training_arg = layer._serialized_attributes["metadata"][
                "expects_training_arg"
            ]
            if "training" in layer_call.function_spec.arg_names:
                # This could change the value of `expects_training_arg` if this
                # layer doesn't expect a training arg, but has a child layer
                # that does.
                expects_training_arg = True
            layer._init_call_fn_args(expects_training_arg)
        else:
            layer.call = types.MethodType(
                _unable_to_call_layer_due_to_serialization_issue, layer
            )

    for layer in layers:
        # 2. Set model inputs and outputs.
        if isinstance(layer, RevivedNetwork):
            _set_network_attributes_from_metadata(layer)

            if hasattr(
                _get_keras_attr(layer), "call_and_return_conditional_losses"
            ):
                call_fn = _get_keras_attr(
                    layer
                ).call_and_return_conditional_losses
                if not call_fn.concrete_functions:
                    continue
                if call_fn.input_signature is None:
                    args, kwargs = infer_inputs_from_restored_call_function(
                        call_fn
                    )
                    args = list(args)
                    inputs = args.pop(0)
                else:
                    args = call_fn.input_signature
                    args = list(args)
                    inputs = args.pop(0)
                    kwargs = None
                layer._set_save_spec(inputs, args, kwargs)

                # V1 models require calling _set_inputs to set the `.inputs`
                # attr.  Skip this step when there are multiple tensor inputs
                # (this behavior is not well supported in V1 models).
                if not any(
                    isinstance(x, tf.TensorSpec)
                    for x in tf.nest.flatten([args, kwargs])
                ):
                    layer._set_inputs(inputs)

        # 3. Add losses that aren't generated by the layer.call function.
        _restore_layer_unconditional_losses(layer)
        _restore_layer_activation_loss(layer)

        # 4. Restore metrics list
        _restore_layer_metrics(layer)


def _unable_to_call_layer_due_to_serialization_issue(
    layer, *unused_args, **unused_kwargs
):
    """Replaces the `layer.call` if the layer was not fully serialized.

    Keras Model/Layer serialization is relatively relaxed because SavedModels
    are not always loaded back as keras models. Thus, when there is an issue
    tracing a non-signature function, a warning is logged instead of raising an
    error. This results in a SavedModel where the model's call function is
    saved, but the internal layer call functions are not.

    When deserialized with `tf.keras.models.load_model`, the internal layers
    which do not have serialized call functions should raise an error when
    called.

    Args:
      layer: Layer without the serialized call function.

    Raises:
      ValueError
    """

    raise ValueError(
        f"Cannot call custom layer {layer.name} of type {type(layer)}, because "
        "the call function was not serialized to the SavedModel."
        "Please try one of the following methods to fix this issue:"
        "\n\n(1) Implement `get_config` and `from_config` in the layer/model "
        "class, and pass the object to the `custom_objects` argument when "
        "loading the model. For more details, see: "
        "https://www.tensorflow.org/guide/keras/save_and_serialize"
        "\n\n(2) Ensure that the subclassed model or layer overwrites `call` "
        "and not `__call__`. The input shape and dtype will be automatically "
        "recorded when the object is called, and used when saving. To manually "
        "specify the input shape/dtype, decorate the call function with "
        "`@tf.function(input_signature=...)`."
    )


def _finalize_config_layers(layers):
    """Runs the final steps of loading Keras Layers from config."""
    for layer in layers:
        # It is assumed that layers define their unconditional losses after
        # being recreated from the config and built. The exceptions to this are
        # Functional and Sequential models, which only store conditional losses
        # (losses dependent on the inputs) in the config. Unconditional losses
        # like weight regularization must be revived from the SavedModel.
        if _is_graph_network(layer):
            _restore_layer_unconditional_losses(layer)

        # Some layers, like Dense, record their activation loss function in the
        # config. However, not all layers do this, so the activation loss may be
        # missing when restored from the config/hdf5.
        # TODO(kathywu): Investigate ways to improve the config to ensure
        # consistent loading behavior between HDF5 and SavedModel.
        _restore_layer_activation_loss(layer)

        # Restore metrics list.
        _restore_layer_metrics(layer)

        # Restore RNN layer states.
        if (
            isinstance(layer, base_rnn.RNN)
            and layer.stateful
            and hasattr(_get_keras_attr(layer), "states")
        ):
            layer.states = getattr(_get_keras_attr(layer), "states", None)
            for variable in tf.nest.flatten(layer.states):
                backend.track_variable(variable)

        # Perform any layer defined finalization of the layer state.
        layer.finalize_state()


def _finalize_metric(metric):
    metric.update_state = types.MethodType(
        metrics_utils.update_state_wrapper(metric.keras_api.update_state),
        metric,
    )
    metric.result = metric.keras_api.result


def _restore_layer_unconditional_losses(layer):
    """Restore unconditional losses from SavedModel."""
    if hasattr(_get_keras_attr(layer), "layer_regularization_losses"):
        losses = getattr(
            _get_keras_attr(layer), "layer_regularization_losses", []
        )
    else:
        # Some earlier SavedModels may not have layer_regularization_losses
        # serialized separately. Fall back to using the regularization_losses
        # list if it does not exist.
        losses = layer._serialized_attributes.get("regularization_losses", [])
    for loss in losses:
        layer.add_loss(loss)


def _restore_layer_activation_loss(layer):
    """Restore actiation loss from SavedModel."""
    # Use wrapped activity regularizer function if the layer's activity
    # regularizer wasn't created during initialization.
    activity_regularizer = getattr(
        _get_keras_attr(layer), "activity_regularizer_fn", None
    )
    if activity_regularizer and not layer.activity_regularizer:
        try:
            layer.activity_regularizer = activity_regularizer
        except AttributeError:
            # This may happen if a layer wrapper is saved with an activity
            # regularizer. The wrapper object's activity regularizer is
            # unsettable.
            pass


def revive_custom_object(identifier, metadata):
    """Revives object from SavedModel."""
    if tf.compat.v1.executing_eagerly_outside_functions():
        model_class = training_lib.Model
    else:
        model_class = training_lib_v1.Model

    revived_classes = {
        constants.INPUT_LAYER_IDENTIFIER: (
            RevivedInputLayer,
            input_layer.InputLayer,
        ),
        constants.LAYER_IDENTIFIER: (RevivedLayer, base_layer.Layer),
        constants.MODEL_IDENTIFIER: (RevivedNetwork, model_class),
        constants.NETWORK_IDENTIFIER: (
            RevivedNetwork,
            functional_lib.Functional,
        ),
        constants.SEQUENTIAL_IDENTIFIER: (
            RevivedNetwork,
            models_lib.Sequential,
        ),
    }
    parent_classes = revived_classes.get(identifier, None)

    if parent_classes is not None:
        parent_classes = revived_classes[identifier]
        revived_cls = type(
            tf.compat.as_str(metadata["class_name"]), parent_classes, {}
        )
        return revived_cls._init_from_metadata(metadata)
    else:
        raise ValueError(
            f"Unable to restore custom object of type {identifier}. "
            "Please make sure that any custom layers are included in the "
            "`custom_objects` arg when calling `load_model()` and make sure "
            "that all layers implement `get_config` and `from_config`."
        )


def _restore_layer_metrics(layer):
    metrics_list = getattr(_get_keras_attr(layer), "layer_metrics", {})
    layer_metrics = {m.name: m for m in layer._metrics}
    for name, metric in metrics_list.items():
        if name not in layer_metrics:
            # Metrics may be added during initialization/building of custom
            # layers.
            layer._metrics.append(metric)


# TODO(kathywu): Centrally define keys and functions for both  serialization and
# deserialization.
class RevivedLayer:
    """Keras layer loaded from a SavedModel."""

    @classmethod
    def _init_from_metadata(cls, metadata):
        """Create revived layer from metadata stored in the SavedModel proto."""
        init_args = dict(name=metadata["name"], trainable=metadata["trainable"])
        if metadata.get("dtype") is not None:
            init_args["dtype"] = metadata["dtype"]
        if metadata.get("batch_input_shape") is not None:
            init_args["batch_input_shape"] = metadata["batch_input_shape"]

        revived_obj = cls(**init_args)

        with utils.no_automatic_dependency_tracking_scope(revived_obj):

            revived_obj._call_spec.expects_training_arg = metadata[
                "expects_training_arg"
            ]
            config = metadata.get("config")
            if generic_utils.validate_config(config):
                revived_obj._config = config
            if metadata.get("input_spec") is not None:
                revived_obj.input_spec = recursively_deserialize_keras_object(
                    metadata["input_spec"],
                    module_objects={"InputSpec": input_spec.InputSpec},
                )
            if metadata.get("activity_regularizer") is not None:
                revived_obj.activity_regularizer = regularizers.deserialize(
                    metadata["activity_regularizer"]
                )
            if metadata.get("_is_feature_layer") is not None:
                revived_obj._is_feature_layer = metadata["_is_feature_layer"]
            if metadata.get("stateful") is not None:
                revived_obj.stateful = metadata["stateful"]
            if metadata.get("autocast") is not None:
                revived_obj._autocast = metadata["autocast"]
            if metadata.get("preserve_input_structure_in_config") is not None:
                revived_obj._preserve_input_structure_in_config = metadata[
                    "preserve_input_structure_in_config"
                ]

        return revived_obj, _revive_setter

    @property
    def keras_api(self):
        return self._serialized_attributes.get(constants.KERAS_ATTR, None)

    def get_config(self):
        if hasattr(self, "_config"):
            return self._config
        else:
            raise NotImplementedError


def _revive_setter(layer, name, value):
    """Setter function that saves some attributes to separate dictionary."""
    # Many attributes in the SavedModel conflict with properties defined in
    # Layer and Model. Save these attributes to a separate dictionary.
    if name in PUBLIC_ATTRIBUTES:

        if isinstance(value, tf.__internal__.tracking.Trackable):
            layer._track_trackable(value, name=name)
        layer._serialized_attributes[name] = value

    elif (
        isinstance(layer, functional_lib.Functional)
        and re.match(r"^layer(_with_weights)?-[\d+]", name) is not None
    ):
        # Edges named "layer-n" or "layer_with_weights-n", which are tracked in
        # network._track_layers, should not be added as an attribute. They
        # should be temporarily added as a dependency so that checkpointed
        # values can be restored. These dependencies are manually deleted in
        # KerasObjectLoader.del_tracking.

        # Set `overwrite=True` in the case that `layer` already tracks a
        # different layer-n. This may cause variable values to not be loaded
        # properly in the original layer-n, but we already warn the users about
        # this (ctrl-f "shared between different layers/models").
        layer._track_trackable(value, name, overwrite=True)
    elif getattr(layer, name, None) is not None:
        # Don't overwrite already defined attributes.
        pass
    else:
        setattr(layer, name, value)


class RevivedInputLayer:
    """InputLayer loaded from a SavedModel."""

    @classmethod
    def _init_from_metadata(cls, metadata):
        """Revives the saved InputLayer from the Metadata."""
        init_args = dict(
            name=metadata["name"],
            dtype=metadata["dtype"],
            sparse=metadata["sparse"],
            ragged=metadata["ragged"],
            batch_input_shape=metadata["batch_input_shape"],
        )
        revived_obj = cls(**init_args)
        with utils.no_automatic_dependency_tracking_scope(revived_obj):
            revived_obj._config = metadata["config"]

        return revived_obj, setattr

    def get_config(self):
        return self._config


def recursively_deserialize_keras_object(config, module_objects=None):
    """Deserialize Keras object from a nested structure."""
    if isinstance(config, dict):
        if "class_name" in config:
            return generic_utils.deserialize_keras_object(
                config, module_objects=module_objects
            )
        else:
            return {
                key: recursively_deserialize_keras_object(
                    config[key], module_objects
                )
                for key in config
            }
    elif isinstance(config, (tuple, list)):
        return [
            recursively_deserialize_keras_object(x, module_objects)
            for x in config
        ]
    else:
        raise ValueError(
            "Unable to decode Keras layer config. Config should be a "
            f"dictionary, tuple or list. Received: config={config}"
        )


def infer_inputs_from_restored_call_function(fn):
    """Returns TypeSpec of inputs from a restored call function.

    Args:
      fn: Restored layer call function. It is assumed that `fn` has at least one
        concrete function and that the inputs are in the first argument.

    Returns:
      TypeSpec of call function inputs in the form of (args, kwargs)
    """

    def common_spec(x, y):
        if not isinstance(x, tf.TypeSpec):
            # Doesn't particularly matter what is returned in this case because
            # the result will be filtered out in _set_input_shape.
            return x

        result = x._without_tensor_names().most_specific_common_supertype(
            [y._without_tensor_names()]
        )
        if result is None:
            # Please file a bug if you are being hindered by this error.
            raise TypeError(f"No common supertype of {x} and {y}.")
        return result

    spec = fn.concrete_functions[0].structured_input_signature
    for concrete in fn.concrete_functions[1:]:
        spec2 = concrete.structured_input_signature
        spec = tf.nest.map_structure(common_spec, spec, spec2)
    return spec


class RevivedNetwork(RevivedLayer):
    """Keras network of layers loaded from a SavedModel."""

    @classmethod
    def _init_from_metadata(cls, metadata):
        """Create revived network from metadata stored in the SavedModel
        proto."""
        revived_obj = cls(name=metadata["name"])

        # Store attributes revived from SerializedAttributes in a un-tracked
        # dictionary. The attributes are the ones listed in CommonEndpoints or
        # "keras_api" for keras-specific attributes.
        with utils.no_automatic_dependency_tracking_scope(revived_obj):

            revived_obj._call_spec.expects_training_arg = metadata[
                "expects_training_arg"
            ]
            config = metadata.get("config")
            if generic_utils.validate_config(config):
                revived_obj._config = config

            if metadata.get("activity_regularizer") is not None:
                revived_obj.activity_regularizer = regularizers.deserialize(
                    metadata["activity_regularizer"]
                )

        return revived_obj, _revive_setter


def _set_network_attributes_from_metadata(revived_obj):
    """Sets attributes recorded in the metadata."""
    with utils.no_automatic_dependency_tracking_scope(revived_obj):

        metadata = revived_obj._serialized_attributes["metadata"]
        if metadata.get("dtype") is not None:
            revived_obj._set_dtype_policy(metadata["dtype"])
        revived_obj._trainable = metadata["trainable"]


def _maybe_add_serialized_attributes(layer, metadata):
    # Store attributes revived from SerializedAttributes in a un-tracked
    # dictionary. The attributes are the ones listed in CommonEndpoints or
    # "keras_api" for keras-specific attributes.
    if not hasattr(layer, "_serialized_attributes"):
        with utils.no_automatic_dependency_tracking_scope(layer):
            layer._serialized_attributes = {"metadata": metadata}


def _get_keras_attr(layer):
    return getattr(layer, "_serialized_attributes", {}).get(
        constants.KERAS_ATTR, None
    )
